Exploring the impact of southern ocean sea ice on the Indian Ocean swells

The present study analyzes the impact of the Southern Ocean (SO) sea ice concentration on the north Indian Ocean (NIO) wave fields through swells using 6 years (2016–2021) of WAVEWATCH III (WWIII) simulations. We did two experimental runs of WWIII, one with sea ice concentration and winds as the forcing (W3with_ice) and the second run with only wind forcing (W3no_ice). Analysis shows the impact of the SO sea ice concentration on northward swell peaks in September–November, coinciding with the maximum sea ice extent in the Antarctic region of the Indian Ocean. W3no_ice simulations are biased more by ~ 60% and ~ 37% in significant wave height and period, respectively, against W3with_ice when compared with NIO mooring data. W3no_ice simulates low-frequency swells and travels fast towards NIO, with implications for operational oceanography. We have shown that the forecasts of the timing of high swell events along NIO coasts can be erroneous by ~ 12 h if the SO sea ice concentration is not included in the model. Further, W3no_ice could potentially produce false swell alerts along southeastern Australian coasts. In summary, our study highlights the importance of the SO sea ice concentration inclusion in the wave models to accurately simulate NIO waves.

www.nature.com/scientificreports/ the south year-round. Generally, there is a convergence of opinion that the SO swells play an important role in determining the wave characteristics of NIO 16 . But, to date, there has not been any study determining the role of sea ice concentration of the SO MIZ in regulating the swell generation and, subsequently, the wave fields of NIO. The regions in and around the IO are home to roughly 2.6 billion people, which is 40% of the global population, out of which one-third are located within 20 km of the coastline 17 . Hence any modifications to wave climate have an important societal impact on their livelihood. For example, the long period swells from the SO cause flash flooding along the Indian coastline, called the Kallakkadal events 18 , creating much distress in the coastal community. The SO swells cause coastal inundations, hindrance to maritime operations, and possible damage to agriculture by contaminating freshwater reservoirs along coasts. Therefore a thorough knowledge of the modulation of NIO swell characteristics by the SO sea ice cover is significant, especially in the wave projection and forecasting context. This article is an effort in that direction to estimate and discuss the impact of the SO sea ice concentration on the NIO swell wave characteristics using WAVEWATCH III (WWIII) modelling tools. The rest of the article is organized as follows. Data description and the methodology are described in section two. The differences between the two WWIII simulations we used in this study are discussed in section three and four. We end this article with a summary and discussion in section five.

Data and methodology
The numerical wave model used in the present study is the WAVEWATCH III-version 6.07 (WWIII) [ 19 based on an algorithm designed to meld the high-resolution bathymetry with the shoreline database to develop the optimum grid. WWIII integrates the spectral wave energy balance equations in space and time with discretized wave numbers and directions. The present study uses a spectral resolution of 29 frequencies ranging from 0.035 to 0.5 Hz at an increment of 10% in 36 directions. The WWIII package includes many dissipation/input parameterizations, of which switch ST4 20 is used in this study. As we plan to quantify the impact of the SO sea ice concentration on the swells in NIO, we used the wave-ice parameterization scheme 21 which provides simple energy flux blocking depending on the local ice concentration.
The wind data used is the 3-hourly European Centre for Medium-range Weather Forecast (ECMWF) winds with a spatial resolution of 0.25° × 0.25°. The daily sea-ice concentration data product from National Snow and Ice Data Centre's Soil Moisture Active Passive (SMAP) L1-L3 Ancillary NOAA Data, Version 1, for 6 years (2016-2021) is used. This dataset, having global coverage and spatial resolution of 0.03° (4 km polar gridded), is used as an input field in WWIII for analyzing the impact of sea ice concentration in the SO on NIO swells.
The model results are then validated against data from six offshore moored buoys from NIO ( Fig. 1), deployed by the National Institute of Ocean Technology (NIOT, Chennai, India) 22

Difference between with and without SO sea ice concentration in wave simulations
We conducted two experiments to quantify the impact of the SO sea ice concentration on the IO wave simulations. In the first experiment, we ran the WWIII model for the 2016-2021 period with the surface wind as the sole forcing field (W3 no_ice ), and in the second, WWIII was run with both winds and the SO sea ice concentration as the input fields (W3 with_ice ). The monthly averaged difference between W3 no_ice and W3 with_ice for a period of 6 years (2016-2021) in significant wave height (Hs), swell height (HsS), mean wave period (Tm), and swell period (TmS) at AD07 (blue bars) and BD08 (red bars) locations are shown in Fig. 2a-d. Figure 2 also shows that the difference in simulations in the total fields (Hs and Tm; Fig. 2a,c) are driven by the swell components (HsS and TmS; Fig. 2b,d), which is quite apparent. The maximum difference between W3 no_ice and W3 with_ice is observed in the SON period. The simulations without sea ice concentration generate low-frequency swells with more wave height in the NIO basins than simulations with sea ice concentration. The maximum monthly mean difference in Hs (HsS) between the two simulations (W3 no_ice − W3 with_ice ) in the NIO was observed in October, approximately 6.5 cm (6-7 cm), respectively. In the September-October period, a similar trend is noted in Tm and TmS in the NIO basins. The impact of sea ice concentration on the SO swell waves is maximum during the September  www.nature.com/scientificreports/ The difference between W3 no_ice and W3 with_ice simulations in the IO is not a clear indication of the accuracy of one over the other. We now compare the two simulations with the NIO mooring observations to see which simulation is closest to the observations. Figure 4 shows the time series comparison of wave parameters from W3 with_ice and W3 no_ice with NIO mooring observations during the SON period of 2017. There are times when there is a clear difference between the two simulations against in-situ data, and the W3 with_ice simulation consistently compares closest to observations. For example, during the 25 September-02 October 2017 period, at the AD07 location, the W3 with_ice simulation reproduced the swell height with ~ 50% less bias than the W3 no_ice  www.nature.com/scientificreports/ simulation. Similar observations are noted in the Bay of Bengal at the BD08 location during the same period. Figure 4b,f also suggest similar results in swell period comparisons. It is noted that W3 no_ice simulations always produce high period swells (low frequency) propagating northwards. The total agreement of the model with the buoy cannot be seen in any of the wave parameters, which is expected due to the discrepancies involved in the forcing wind, ice concentration fields, the model parameterization scheme, etc. Table 1 provides the average statistics of W3 with_ice and W3 no_ice simulations against the six NIO mooring observations during the SON period of 6 years (2016-2021). The statistics shown are 95% significant (p = 0.05) using Student's t-test. In all the NIO mooring locations we considered in this study, W3 no_ice HsS simulation biases are 8-10 cm more than W3 with_ice simulations compared to observations. We found that W3 no_ice is ~ 60% (37%) more biased than W3 with_ice simulations in HsS (TmS) averaged over the NIO mooring locations. A comparison of Tm and TmS suggests that W3 no_ice produces more low frequency (peak shift towards lower frequencies) swell waves compared to the swells in W3 with_ice . The spectral energy data available for September 2017 at the BD08 location is used for the energy comparison between the two model simulations (Fig. 5a,b). The spectral peaks at low frequencies indicate swells, and W3 no_ice overestimates the energy density peak at ~ 0.07 Hz on 27th September 2017. Whereas W3 with_ice agrees well with observation, both models underestimate the low-frequency peak on 7th September, although W3 with_ice is better in comparison. At higher frequencies (> 0.15 Hz), W3 no_ice and W 3with_ice behave almost similarly. Overall, W3 with_ice is better at predicting low-frequency swells than the other simulation in the NIO basins.
In-situ observations are unavailable from the SO for wave field comparison between the two simulations. One possible option to ascertain the quality of wave simulations in the SO is to compare the Hs from the satellite with the model simulations. Figure 6 shows the satellite Hs comparison with W3 with_ice and W3 no_ice simulations for the ice-free regions of the SO (55-40° S, 40-110° E) for SON of 2017. Both W3 with_ice and W3 no_ice simulations are

Implications of SO sea ice concentration on wave simulations
In the previous section, we presented the numerical difference in the wave fields between W3 with_ice and W3 no_ice simulations and found that W3 with_ice simulates accurate wave parameters. This section shows the local and remote implications of the SO sea ice concentration inclusion as an input field in the wave simulations. Figure 3d-g show the average sea ice concentration for different seasons used to force the model to run the W3 with_ice simulation. Around 1,00,00 km 2 area is covered with sea ice concentration in the southern sector of IO during SON, which acts as a large fetch area for wave generation in W3 no_ice simulation. The area around 30-50° E and 70-50° S is considered a strong swell generating area 14 with a fetch size of ~ 44,000 km 2 . In W3 no_ice , the entire 44,000 km 2 is available as fetch for swell generation, while in W3 with_ice , ~ 46% of the area is ice-covered during SON (see Fig. 3g). Considering a fetch area of 44,000 km 2 , a friction velocity of 0.46 m/s at an average wind speed of 12 m/s, the average Hs calculated will be 1.27 m in W3 no_ice using the following equation: where X is the fetch area, g is the acceleration due to gravity, u f is the friction velocity, and Hs is the significant wave height. Repeating the wave height calculation for W3 with_ice simulation with 46% less fetch area produces a wave height of 0.86 m. A fetch reduction of ~ 46% on introducing sea ice concentration causes a 32% reduction in wave height in W3 with_ice . These calculations agree with the satellite Hs comparisons in the previous section, where W3 no_ice simulated higher Hs compared to W3 with_ice simulations. Following the classical wave theory, the group velocity of deep water waves is Cg = 0.78 T m/s, where T is the wave period. On a general note, taking the average difference in swell wave period between W3 no_ice and W3 with_ice as 0.15 s (Fig. 2b), the ratio of Cg between W3 no_ice and W3 with_ice suggests that W3 no_ice swells travel faster by ~ 11.5% than W3 with_ice swells. This speed difference has significant implications. Most SO swell waves travel ~ 9000 km to reach NIO coastal regions and have a typical phase speed of ~ 20 m/s. Thus, the SO swells take ~ 5.2 days to reach the NIO coastal regions in W3 with_ice simulations. An 11.5% increase in the phase speed in W3 no_ice compared to W3 with_ice suggests that W3 no_ice swells take only 4.67 days to reach NIO coastal regions. The difference of ~ 12 h in predicting the SO swell impact on NIO coastal regions can sometimes be disastrous in operational wave forecasting.
Here we consider a typical case of a swell system that was generated in the SO on 7th October 2017 and travelled northwards into the NIO. Figure 7a,b show the approximate origin of the swell system in the SO in W3 with_ice and W3 no_ice simulations. The straight line indicates the typical distance the swell wave travelled, ~ 6400 km, to reach the AD07 mooring location in the Arabian Sea, and the dots in the line indicate the distance the swell system reached along the straight line every 12 h. Figure 7c shows the distance vs. time of the swell system in W3 no_ice and W3 with_ice over the next few days until it reached the AD07 location. The W3 no_ice swell system travelled faster than W3 with_ice and reached the mooring location on 10th October 2018 at 21 h, while W3 with_ice swells were slower and reached AD07 on 11th October 2018 at 9 h. Thus there is a net difference of 12 h between the swell system of www.nature.com/scientificreports/ W3 with_ice and W3 no_ice to travel from its source region in the SO to reach the AD07 location. These observations are in agreement with our theoretical calculations provided above. In the previous section, a comparison of the two simulations with observations suggests that W3 no_ice produces low-frequency swells that travel faster. Here, we have shown the typical case of such a system with an arrival time difference of ~ 12 h at the AD07 location. Another noticeable feature of Fig. 7a,b is the arc length of the swell system generated from the southwestern sector of the IO. The swell generating area of the IO is concentrated mainly on the southwest side of the SO 13 , where the sea ice concentration undergoes a prominent seasonal cycle (Fig. 3d-g.). Due to the larger fetch area, W3 no_ice produces a much bigger swell system that sweeps over a wider geographical extent of the eastern portion of IO, especially the southwestern coastal regions of Australia. The arc length of W3 with_ice is shorter than W3 no_ice and has limited impact along Australian coasts. Thus W3 no_ice could potentially produce false swell alerts along such regions.
In short, our analysis in this section shows that the ice cover in the SO will reduce the fetch, affecting wave growth. We also see a reduction in the swell wave period if sea ice concentration is included in the SO. The decrease in the wave period will change the arrival time of such swells on NIO coasts, which is critical from a forecasting point of view 18 . Failure to accurately predict the timing of such swell surges, which usually do not have any local weather changes along NIO coasts, can pose a significant threat to the lives and livelihoods of the coastal population.

Summary and discussion
This article explores the effect of the Southern Ocean (SO) sea ice concentration on the Indian Ocean (IO) wave conditions using WAVEWATCHIII (WWIII) numerical wave model simulations. The impact of the SO sea ice concentration on IO swells is not explicitly addressed in scientific literature. However, past studies on IO www.nature.com/scientificreports/ wave climate proved the effect of SO swells on IO coastal areas. In the present study, we did two WWIII model simulations, one with winds and the SO sea ice concentration as forcing fields (W3 with_ice ) and the other with only winds as forcing (W3 no_ice ), to explore the effect of the SO sea ice concentration on wave simulations. Our analysis suggests a non-negligible difference in HsS and TmS in the SO (Fig. 2b,d). Validations with NIO mooring data suggest that wave simulations with the SO sea ice concentration accurately reproduce the observed wave characteristics, both Hs and Tm. The impact of sea ice concentration on the SO swell waves is maximum during the September-October-November (SON) season and minimum during March-April-May (MAM), coinciding positively with the spatial variation of sea ice extent in the SO. The impact of sea ice concentration is reflected as a few centimeters (~ 6-8 cm; Fig. 3c) change in swells height in the NIO wave simulations. Generally, W3 no_ice simulates low-frequency swells that travel fast towards NIO. Our analysis suggests that WWIII simulations with sea ice concentration accurately forecast the timing of high swell events in the NIO coastal regions. We tracked a SO swell system formed on 7th October 2018, in the two simulations and analyzed the arrival time at the AD07 mooring location in the Arabian Sea, which is ~ 6400 km from the swell wave formation. We found that swells from W3 wtih_ice reached first at AD07 in 105 h, while swells from W3 no_ice simulation took 93 h to arrive. This difference of ~ 12 h in the arrival time is also proved theoretically in "Implications of SO sea ice concentration on wave simulations section. Due to the larger fetch area, W3 no_ice produces a much bigger swell system that sweeps over a wider geographical extent of the eastern portion of IO, especially the southwestern coastal regions of Australia. Hence it is presumed that W3 no_ice could potentially produce false swell alerts along such southeastern Australian coasts. Overall, our analysis suggests that W3 wtih_ice simulates well the NIO swells. Accurate forecasts of swell surges and catastrophic events like Kallakkadal will help the disaster management authorities to coordinate well with the local civil administration to handle the response activities in the coastal regions. Moreover, it asserts faith in the coastal population to take such forecasts seriously and corporate with the response coordination of the disaster management authorities.
A potential caveat of our analysis is the lack of model validation in the SO due to the non-availability of in situ observations in such remote regions. Comparing simulated wave fields with NIO mooring observations suggests a good correlation and minimum bias in wave fields. This comparison indirectly indicates that the swell simulation from the SO is probably correct, an inference positively supported by the satellite Hs comparison presented in this paper. Further, our analysis included the year 2017, the record low in Southern hemisphere sea ice extent. A long-term analysis may be required beyond the 6 years (2016-2021) we used here.
WWIII employs several wave-ice parameterization schemes (like IC0, IC1, IC2, IC3, IC4, and IC5) to account for the effect of sea ice concentration in wave simulations 23 . We did not focus on validating different waveice parameterization schemes here but selected the simple IC0 scheme, which provides energy flux blocking depending on the local ice concentration and does not consider the thickness of the ice. Studies suggest that the simulation of wave fields under ice-covered conditions relies on the accuracy of sea ice concentration data used as a model forcing and the accuracy of the wave-ice parameterization 23 . Future studies may require validating the sea ice concentration data and the wave-ice parameterizations.
The sea ice extent in different seasons in the Indian sector of the SO varies largely, from a minimum of 74,208.5 km 2 in DJF to a maximum of 1,08,308.75 km 2 in SON, and a wave model without this large sea ice extent will grossly overestimate the wave growth. Hence our arguments in this article will be valid irrespective of the above caveats, and any change in the ice extent in the SO will substantially impact NIO through the SO swells. Thus our study recognizes sea ice concentration in the SO as a critical factor in modifying wave characteristics in the NIO and underscores the need to include it in wave modeling for forecasting and climate simulations.